Releases · NVIDIA/DALI

29 Aug 19:14

stiepan

v1.41.0

040b354

DALI v1.41.0 Latest

Latest

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added support for CUDA 12.6. (#5596)
Added fn.experimental.warp_perspective operator. (#5542, #5575)
Added fn.random.beta random variate sampling operator. (#5550, #5571)
Added fn.io.file.read operator that supports loading files from dynamically specified paths. (#5552, #5572)
Enabled support for more simple types in fn.python_function, fn.ones, and fn.zeros. (#5598)
Removed unnecessary copy of tensor arguments fed into GPU operators. (#5590)

Fixed Issues

Reverted the fn.decoders.image* to use legacy decoders due to performance regression in nvImageCodec. (#5582, #5578, #5586)
Optimized S3 downloading in TFRecord reader. (#5554)
Added missing validation for number of inputs in argument promotion. (#5592)
Added missing header to support compilation with GCC 14. (#5594)
Fixed empty batch handling when copying batch from cpu to gpu. (#5567)

Improvements

Executor 2.0: ExecGraph (#5587)
Enable more Python types to be supported by the DALI python function (#5598)
Remove usages of std::call_once. (#5599)
Move to CUDA 12.6 (#5596)
Remove MakeContiguous before CPU inputs of GPU ops. (#5590)
nvImageCodec related fixes (#5586)
Mark PropagateError as [[noreturn]] (#5589)
Make test_beta_distribution compatible with Python 3.8 (#5571)
Add default_batch_size to IterationData. (#5588)
Add thread_setup callback to tasking::Executor (#5581)
Fix librosa deprecated usage (#5579)
Bring back the legacy image decoder operator (#5578)
Extract librosa's effects.trim and stft to DALI test utils, to avoid issues with breaking changes (#5568)
Remove libjpeg and libtiff deps (#5569)
Add warp_perspective operator (#5542)
Remove legacy image decoder (#5559)
Optimize S3 downloading for TFRecord reader (#5554)
Add io.file.read operator (#5552)
Add fn.random.beta random variate (#5550)
Reduce the batch size in the TensorFlow RN50 L3 test (#5565)
Use MakeContiguous when copying CPU->CPU. (#5562)
Update the DALI EfficientNet example to be compatible with the latest NumPy (#5561)

Bug Fixes

Fixes problems with fetching LFS objects during nvImageCodec conda build (#5603)
Fix the --python-tag option passed to python setup.py bdist_wheel command (#5600)
Revert "Reintroduce "Move old ImageDecoder to legacy module and make the nvImageCodec based ImageDecoder the default" (#5470)" (#5582)
Adding cstdint header to support GCC 14 compilation (#5594)
Add missing validation for input count in argument promotion (#5592)
Don't return pointers to a local variable in dali_operator_test. (#5585)
Fix operator trace caching (#5580)
Fix readlink usage - readlink doens't null-terminate strings. (#5577)
Fix WarpPerspective::GetFillValue (#5575)
Prevent stack-use-after-scope (#5572)
Add missing #include <optional> in nvcvop.h (#5570)
Fix MakeContiguous sample_dim for empty batches. (#5567)
Set affinity by device UUID. (#5566)
Unchecked return value from CUDA library (#5564)

Breaking API changes

DALI 1.39 was the final release to support the MXNet integration.

Deprecated features

No features were deprecated in this release.

Known issues:

The following operators: experimental.readers.fits, experimental.decoders.video, and experimental.inputs.video do not currently support checkpointing.
The video loader operator requires that the key frames occur, at a minimum, every 10 to 15 frames of the video stream.
If the key frames occur at a frequency that is less than 10-15 frames, the returned frames might be out of sync.
Experimental VideoReaderDecoder does not support open GOP.
It will not report an error and might produce invalid frames. VideoReader uses a heuristic approach to detect open GOP and should work in most common cases.
The DALI TensorFlow plugin might not be compatible with TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have a prebuilt plugin binary shipped with DALI, make sure that the compiler that is used to build TensorFlow exists on the system during the plugin installation. (Depending on the particular version, you can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
In experimental debug and eager modes, the GPU external source is not properly synchronized with DALI internal streams.
As a workaround, you can manually synchronize the device before returning the data from the callback.
Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows best performance when running in Docker with escalated privileges, for example:
- privileged=yes in Extra Settings for AWS data points
- --privileged or --security-opt seccomp=unconfined for bare Docker.

Binary builds

NOTE: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

CUDA 11.0 and CUDA 12.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 11.x/12.x toolkit respectively but they can run on the latest, 
stable CUDA 11.0/CUDA 12.0 capable drivers (450.80 or later and 525.60 or later respectively).
However, using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 12.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.41.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.41.0

or just:

pip install nvidia-dali-cuda120==1.41.0
pip install nvidia-dali-tf-plugin-cuda120==1.41.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.41.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.41.0

or just:

pip install nvidia-dali-cuda110==1.41.0
pip install nvidia-dali-tf-plugin-cuda110==1.41.0

Or use direct download links (CUDA 12.0):

Or use direct download links (CUDA 11.0):

FFmpeg source code:

This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

https://developer.download.nvidia.com/compute/redist/nvidia-dali/libsndfile-1.2.2.tar.gz

Assets 2

31 Jul 11:06

stiepan

v1.40.0

e271d13

DALI v1.40.0

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added operators: fn.zeros , fn.zeros_like , fn.ones , fn.ones_like , fn.full and fn.full_like (#5505).
Added support for H264, H265, and AV1 video formats to fn.plugin.video (#5504).
Added support for CUDA 12.5U1 (#5545).

Fixed Issues

Fixed following issues with S3 files reading:
- Fixed handling of file names with whitespaces in TFRecord reader (#5525).
- Fixed loading when no GPU is available (#5533).
- Fixed handling of TFrecord index file (#5515).

Improvements

Dependency update 07/2024 (#5556)
Move checkpoint to IterationData. Remove ExecIterData. (#5555)
Remove pruning from the Executor. (#5553)
Move most of Operator to OperatorBase. Unify and simplify operator interfaces. (#5548)
Move graph visiting utilities to a separate file. (#5549)
Move to CUDA 12.5U1 (#5545)
Extend the external source signature to include all arguments (#5541)
Update DALI_deps version (#5536)
Pin numpy to <1.24 in TensorFlow examples (#5534)
Use new graph in Pipeline (#5520)
Deps update 06/24 (#5514)
Revert reducing the number of epoch in SBSA training test case (#5531)
Add AV1 support (#5504)
Removes MXNet support from DALI (#5526)
Video decoder in plugin (#5477)
Checkpoint refactoring - recognize checkpoints by operator instance name. (#5503)
Keep separate per-pipeline operator counters. Error out when "stealing" subgraphs from other pipelines results in duplicate names. (#5506)
Graph lowering. (#5496)
Use "device" and "preserve" built-in arguments in OpGraph2. (#5516)
Add fn.zeros, fn.zeros_like, fn.ones, fn.ones_like, fn.full and fn.full_like (#5505)

Bug Fixes

Support spaces in S3 paths (#5525)
Fix device ID in s3_client_manager (#5533)
Add failure tests for stealing subgraphs. Minor fix in pipeline validation. (#5518)
TFRecord to support S3 index URIs (#5515)
Exclude docs line length adjustment PR from the blame history (#5509)
Fix keras compat mode for ResNet50 tensorflow example (#5530)

Breaking API changes

DALI 1.39 was the final release to support the MXNet integration.

Deprecated features

No features were deprecated in this release.

Known issues:

Starting with DALI 1.39, a performance regression was observed in hardware-accelerated image decoders for setups with high number of worker threads. The nvImageCodec hardware decoder pre-allocation uses higher mini-batch size, causing extra cuMemFree calls that may slowing down decoding in some iterations. The issue will be fixed in the upcoming release.
The following operators: experimental.readers.fits, experimental.decoders.video, and experimental.inputs.video do not currently support checkpointing.
The video loader operator requires that the key frames occur, at a minimum, every 10 to 15 frames of the video stream.
If the key frames occur at a frequency that is less than 10-15 frames, the returned frames might be out of sync.
Experimental VideoReaderDecoder does not support open GOP.
It will not report an error and might produce invalid frames. VideoReader uses a heuristic approach to detect open GOP and should work in most common cases.
The DALI TensorFlow plugin might not be compatible with TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have a prebuilt plugin binary shipped with DALI, make sure that the compiler that is used to build TensorFlow exists on the system during the plugin installation. (Depending on the particular version, you can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
In experimental debug and eager modes, the GPU external source is not properly synchronized with DALI internal streams.
As a workaround, you can manually synchronize the device before returning the data from the callback.
Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows best performance when running in Docker with escalated privileges, for example:
- privileged=yes in Extra Settings for AWS data points
- --privileged or --security-opt seccomp=unconfined for bare Docker.

Binary builds

NOTE: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

CUDA 11.0 and CUDA 12.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 11.x/12.x toolkit respectively but they can run on the latest, 
stable CUDA 11.0/CUDA 12.0 capable drivers (450.80 or later and 525.60 or later respectively).
However, using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 12.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.40.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.40.0

or just:

pip install nvidia-dali-cuda120==1.40.0
pip install nvidia-dali-tf-plugin-cuda120==1.40.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.40.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.40.0

or just:

pip install nvidia-dali-cuda110==1.40.0
pip install nvidia-dali-tf-plugin-cuda110==1.40.0

Or use direct download links (CUDA 12.0):

Or use direct download links (CUDA 11.0):

FFmpeg source code:

This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

https://developer.download.nvidia.com/compute/redist/nvidia-dali/libsndfile-1.2.2.tar.gz

Assets 2

28 Jun 13:53

stiepan

v1.39.0

1238d5d

DALI v1.39.0

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added support for CUDA 12.5 (#5478).
Migrated fn.decoders.image* operators to use nvImageCodec as a decoding backend (#5470).
Improved error handling (#5466, #5494, #5486, #5491).

Fixed Issues

Fixed DALI TF plugin compatibility with TensorFlow 2.9 (#5499).
Fixed S3 fn.readers.file support for pad_last_batch=True (#5493).
Fixed a bug that resulted in long build times for some pipelines with enabled conditional execution (#5475).

Improvements

Add a mention of blogpost in Automatic Augmentation docs (#5508)
Removal of Python 3.8 notes from documentation (#5502)
Add default schema and use it in OpSpec argument queries. (#5500)
Add missing blocking argument documentation to the external source operator (#5501)
Trim line length in the documentation/examples for the new theme (#5479)
Refactoring in Pipeline, OpGraph and old Executor + name lookup improvement in old OpGraph and Pipeline. (#5495)
Improve error message about FFmpeg not being available (#5494)
Extend docs by adding info about @do_not_convert for NUMBA and Python ops (#5488)
New OpGraph (#5485)
Fix tests for sanitizer build (#5492)
Github comment acceptance formating table fix (#5490)
Remove image decoder memory padding from examples (#5484)
Adding git lfs as a compilation prerequisite (#5483)
Remove unused JIT workspace policy. (#5487)
Add a warning about pipeline definition being executed only once. (#5486)
Move to CUDA 12.5 (#5478)
Pin NPP version for CUDA 12 (#5480)
Reintroduce "Move old ImageDecoder to legacy module and make the nvImageCodec based ImageDecoder the default" (#5470)
Move to new, unified, NVIDIA sphinx theme (#5471)
Add DALI video plugin skeleton (#5328)
Don't initialize NVML when not setting affinity. (#5472)
Add MXNet deprecation message to the docs and plugin (#5465)
Add first-class check for nested datanodes in math/arithmetic ops. (#5466)

Bug Fixes

Fix DALI TF plugin incompatibility with TF 2.9 (#5499)
Coverity May 2024 (#5497)
Fix S3 FileReader when using repeated samples (pad_last_batch=True) (#5493)
Improve the video decoder errors (#5491)
Add extra rpath for prebuilt ffmpeg dependencies for video plugin (#5481)
Use dynamic programming in OpGraph::HasConsumersInOtherStage (#5475)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

DALI 1.39 is the final release that will support the MXNet integration.

Known issues:

The following operators: experimental.readers.fits, experimental.decoders.video, and experimental.inputs.video do not currently support checkpointing.
The video loader operator requires that the key frames occur, at a minimum, every 10 to 15 frames of the video stream.
If the key frames occur at a frequency that is less than 10-15 frames, the returned frames might be out of sync.
Experimental VideoReaderDecoder does not support open GOP.
It will not report an error and might produce invalid frames. VideoReader uses a heuristic approach to detect open GOP and should work in most common cases.
The DALI TensorFlow plugin might not be compatible with TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have a prebuilt plugin binary shipped with DALI, make sure that the compiler that is used to build TensorFlow exists on the system during the plugin installation. (Depending on the particular version, you can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
In experimental debug and eager modes, the GPU external source is not properly synchronized with DALI internal streams.
As a workaround, you can manually synchronize the device before returning the data from the callback.
Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows best performance when running in Docker with escalated privileges, for example:
- privileged=yes in Extra Settings for AWS data points
- --privileged or --security-opt seccomp=unconfined for bare Docker.

Binary builds

NOTE: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

CUDA 11.0 and CUDA 12.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 11.x/12.x toolkit respectively but they can run on the latest, 
stable CUDA 11.0/CUDA 12.0 capable drivers (450.80 or later and 525.60 or later respectively).
However, using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 12.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.39.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.39.0

or just:

pip install nvidia-dali-cuda120==1.39.0
pip install nvidia-dali-tf-plugin-cuda120==1.39.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.39.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.39.0

or just:

pip install nvidia-dali-cuda110==1.39.0
pip install nvidia-dali-tf-plugin-cuda110==1.39.0

Or use direct download links (CUDA 12.0):

Or use direct download links (CUDA 11.0):

FFmpeg source code:

This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

https://developer.download.nvidia.com/compute/redist/nvidia-dali/libsndfile-1.2.2.tar.gz

Assets 2

29 May 09:42

stiepan

v1.38.0

8f2a43f

DALI v1.38.0

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added support for AWS S3 urls in DALI readers (#5415, #5434).
Improved support for enum types in types.Constant, fn.cast, fn.random.choice (#5422).
Improved error reporting (#5428).

Fixed Issues

Fixed checkpoint clean-up in C API. (#5453)

Improvements

Dependency update for May 2024 - black, boost-pp, cv-cuda, pybind11, rapidjson (#5458)
Introduce DALI_PRELOAD_PLUGINS (#5457)
Move old ImageDecoder to legacy module and make the nvImageCodec based ImageDecoder the default (#5445)
Bump up NUMBA version used in tests to 0.59.1 (#5451)
Extend the documentation footer (#5454)
Remove the use of (soon deprecated) aligned_storage. (#5455)
Make shared IterationData a first class member of Workspace. (#5447)
Tasking module (#5436)
Add AWS SDK support to all file readers (FileReader, NumpyReader, WebdatasetReader...) (#5415)
Fix test_enum_types.py for Python3.11 (#5443)
Remove files related to QNX that are no longer used (#5438)
Remove usage of THRUST host&device vector (#5439)
Add CMake to aarch64 base docker images (#5437)
Refactoring of File Reader classes to accommodate for AWS SDK S3 integration (#5434)
Replace Ops class name with proper operator API name (#5428)
Use CMake binary release (#5435)
Improve support for DALI enum types (#5422)
Disable some JAX iterator tests in sanitizer run (#5427)

Bug Fixes

Fix GTest Death Style Tests and LoadDirectory test in conda (#5469)
Revert "Move old ImageDecoder to legacy module and make the nvImageCodec based ImageDecoder the default (#5445)" (#5464)
Pin JAX version for multigpu test (#5460)
Use C++17 standard in nodeps test. (#5459)
Fix Coverity issues (May/2024) (#5453)
Fix equalize unit test (#5456)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

DALI 1.39 will be the last release to support MXNet integration.

Known issues:

The following operators: experimental.readers.fits, experimental.decoders.video, experimental.inputs.video, and experimental.decoders.image_random_crop do not currently support checkpointing.
The video loader operator requires that the key frames occur, at a minimum, every 10 to 15 frames of the video stream.
If the key frames occur at a frequency that is less than 10-15 frames, the returned frames might be out of sync.
Experimental VideoReaderDecoder does not support open GOP.
It will not report an error and might produce invalid frames. VideoReader uses a heuristic approach to detect open GOP and should work in most common cases.
The DALI TensorFlow plugin might not be compatible with TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have a prebuilt plugin binary shipped with DALI, make sure that the compiler that is used to build TensorFlow exists on the system during the plugin installation. (Depending on the particular version, you can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
In experimental debug and eager modes, the GPU external source is not properly synchronized with DALI internal streams.
As a workaround, you can manually synchronize the device before returning the data from the callback.
Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows best performance when running in Docker with escalated privileges, for example:
- privileged=yes in Extra Settings for AWS data points
- --privileged or --security-opt seccomp=unconfined for bare Docker.

Binary builds

NOTE: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

CUDA 11.0 and CUDA 12.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 11.x/12.x toolkit respectively but they can run on the latest, 
stable CUDA 11.0/CUDA 12.0 capable drivers (450.80 or later and 525.60 or later respectively).
However, using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 12.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.38.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.38.0

or just:

pip install nvidia-dali-cuda120==1.38.0
pip install nvidia-dali-tf-plugin-cuda120==1.38.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.38.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.38.0

or just:

pip install nvidia-dali-cuda110==1.38.0
pip install nvidia-dali-tf-plugin-cuda110==1.38.0

Or use direct download links (CUDA 12.0):

Or use direct download links (CUDA 11.0):

FFmpeg source code:

This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

https://developer.download.nvidia.com/compute/redist/nvidia-dali/libsndfile-1.2.2.tar.gz

Assets 2

07 May 16:55

JanuszL

v1.37.1

d1685ac

DALI v1.37.1

Key Features and Enhancements

There are no new features in this release

Fixed Issues

Fixed DALI TF plugin source compilation during installation #5448

Improvements

There are no new improvements in this release

Bug Fixes

Fixed DALI TF plugin source compilation during installation #5448
Pin all nvJPEG2k subpackages #5442

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this release.

Known issues:

The following operators: experimental.readers.fits, experimental.decoders.video, experimental.inputs.video, and experimental.decoders.image_random_crop do not currently support checkpointing.
The video loader operator requires that the key frames occur, at a minimum, every 10 to 15 frames of the video stream.
If the key frames occur at a frequency that is less than 10-15 frames, the returned frames might be out of sync.
Experimental VideoReaderDecoder does not support open GOP.
It will not report an error and might produce invalid frames. VideoReader uses a heuristic approach to detect open GOP and should work in most common cases.
The DALI TensorFlow plugin might not be compatible with TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have a prebuilt plugin binary shipped with DALI, make sure that the compiler that is used to build TensorFlow exists on the system during the plugin installation. (Depending on the particular version, you can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
In experimental debug and eager modes, the GPU external source is not properly synchronized with DALI internal streams.
As a workaround, you can manually synchronize the device before returning the data from the callback.
Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows best performance when running in Docker with escalated privileges, for example:
- privileged=yes in Extra Settings for AWS data points
- --privileged or --security-opt seccomp=unconfined for bare Docker.

Binary builds

NOTE: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

CUDA 11.0 and CUDA 12.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 11.x/12.x toolkit respectively but they can run on the latest, 
stable CUDA 11.0/CUDA 12.0 capable drivers (450.80 or later and 525.60 or later respectively).
However, using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 12.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.37.1
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.37.1

or just:

pip install nvidia-dali-cuda120==1.37.1
pip install nvidia-dali-tf-plugin-cuda120==1.37.1

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.37.1
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.37.1

or just:

pip install nvidia-dali-cuda120==1.37.1
pip install nvidia-dali-tf-plugin-cuda120==1.37.1

Or use direct download links (CUDA 12.0):

Or use direct download links (CUDA 11.0):

FFmpeg source code:

This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

https://developer.download.nvidia.com/compute/redist/nvidia-dali/libsndfile-1.2.2.tar.gz

Assets 2

29 Apr 15:41

stiepan

v1.37.0

1bc7fc2

DALI v1.37.0

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added support for running JAX defined augmentations in the iterator and pipeline. (#5406, #5426, #5432)
Improved error reporting with a stack trace pointing to the offending operation in user code. (#5357, #5396)
Added CPU fn.random.choice operator. (#5380, #5387)
Added support for CUDA 12.4. (#5353, #5410)
Improved iterators checkpointing (#5374, #5375, #5371, #5356)
Optimized fn.resize operator for better GPU utilization (#5382)
Added option to skip bboxes in fn.random_bbox_crop with the fraction of area within the crop below user-provided threshold. (#5368)

Fixed Issues

Fixed handling of special values of the stream field in CUDA Array Interface v3 (#5425).
Fixed insufficient synchronization around scratch memory in nvImageCoded-based decoders (fn.experimental.decoders.*). (#5408)
Fixed readers saving incorrect checkpoint when restored and saved back in the same epoch. (#5378)

Improvements

Add JAX-defined augmentation examples (#5426)
Extend context and name propagation in errors (#5396)
Add experimental jax operator (#5406)
Enable Bandit security scan (#5402)
Reworks links in the RST documentation (#5413)
Refactor to remove duplicated logic in traverse_directories utility function (#5419)
Update DALI deps version (#5417)
Changes to dali/util/numpy (#5416)
Add libcurl-devel (#5412)
Move to CUDA 12.4 U1 (#5410)
Separate excutor interface and implementation files. (#5411)
Make the video reader use cudaVideoDeinterlaceMode_Adaptive only for non-progressive videos (#5392)
Skip AutoAug test when sanitizers are on (#5403)
Unpin typing_extensions in tests (#5405)
Dependency update 03-2024 (#5397)
Review Bandit reported vulnerabilities (#5398)
Support checkpointing in JAX decorators (#5374)
Workaround ASAN bug ignoring RPATH (#5388)
Update supported TensorFlow version (#5386)
Disable more video tests on selected machines (#5385)
Extend fn.random.choice to support n-D inputs (#5387)
Add random choice CPU operator for 0D samples (#5380)
Resize: Optimize block sizes, use dynamic amount of shared mem. (#5382)
Support checkpointing in JAX peekable iterator (#5375)
Increase DALI TF Plugin loading timeout (#5381)
Improve iterator checkpointing (#5371)
Improve logs when the DALI TF plugin loading process fails (#5379)
Add option to prune bboxes based on % area in Crop ROI (#5368)
Improve op deprecation and deprecate sequence reader (#5372)
Fix typo in nvcuvid error (#5373)
Optimize sanitizer operator tests (#5352)
Introduce operator origin stack trace in the error message (#5357)
Make ExternalContext more flexible (#5356)
Enable CUDA 12.4 build (#5353)

Bug Fixes

Add nose as a dependency to iterators tests (#5433)
Disable jax_function notebook conversions for unsupported Python3.8 (#5432)
Improve handling of CUDA Array Interface v3 (#5425)
Fix debug build (#5414)
Add stream synchronization before decode for nvImageCodec <= 0.2 (#5408)
Fix Loader checkpointing bug (#5378)
Fix pixelwise_masks support when the ratio is on in the coco reader (#5407)
Fix storage of non-POD random distributions. (#5395)
Fix nvImageCodec version check. (#5399)
Fix bug in checkpointing C API (#5390)
Add nose to the package list for TL1_separate_executor. (#5393)
Use host sync allocation for nvImageCodec <= 0.2 (#5391)
Remove temporary lock file from wheel (#5384)
Disable type annotation tests in sanitizer build (#5383)
Fix CUDA 12.4 with ASAN (#5370)
Skip video tests on M60 (#5369)
Enable eager mode tests, fix mixed ops and improve coverage (#5367)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this release.

Known issues:

The following operators: experimental.readers.fits, experimental.decoders.video, experimental.inputs.video, and experimental.decoders.image_random_crop do not currently support checkpointing.
The video loader operator requires that the key frames occur, at a minimum, every 10 to 15 frames of the video stream.
If the key frames occur at a frequency that is less than 10-15 frames, the returned frames might be out of sync.
Experimental VideoReaderDecoder does not support open GOP.
It will not report an error and might produce invalid frames. VideoReader uses a heuristic approach to detect open GOP and should work in most common cases.
The DALI TensorFlow plugin might not be compatible with TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have a prebuilt plugin binary shipped with DALI, make sure that the compiler that is used to build TensorFlow exists on the system during the plugin installation. (Depending on the particular version, you can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
In experimental debug and eager modes, the GPU external source is not properly synchronized with DALI internal streams.
As a workaround, you can manually synchronize the device before returning the data from the callback.
Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows best performance when running in Docker with escalated privileges, for example:
- privileged=yes in Extra Settings for AWS data points
- --privileged or --security-opt seccomp=unconfined for bare Docker.

Binary builds

NOTE: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

CUDA 11.0 and CUDA 12.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 11.x/12.x toolkit respectively but they can run on the latest, 
stable CUDA 11.0/CUDA 12.0 capable drivers (450.80 or later and 525.60 or later respectively).
However, using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 12.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.37.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.37.0

or just:

pip install nvidia-dali-cuda120==1.37.0
pip install nvidia-dali-tf-plugin-cuda120==1.37.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.37.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.37.0

or just:

pip install nvidia-dali-cuda120==1.37.0
pip install nvidia-dali-tf-plugin-cuda120==1.37.0

Or use direct download links (CUDA 12.0):

Or use direct download links (CUDA 11.0):

FFmpeg source code:

This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

https://developer.download.nvidia.com/compute/redist/nvidia-dali/libsndfile-1.2.2.tar.gz

Assets 2

25 Mar 18:23

stiepan

v1.36.0

e2ae685

DALI v1.36.0

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added support for checkpointing in MXNet iterator and CPU TensorFlow plugin (#5334, #5315).
Added morphological operators (fn.experimental.dilate, fn.experimental.erode) (#5294).
Integrated nvImageCodec for decoding in fn.experimental.decoders (#5297, #5336, #5324, #5333, #5339).
Added fn.random_crop_generator operator (#5304).
Added support for multiple inputs and relative shapes and anchors in fn.multi_paste (#5331).

Fixed Issues

Fixed insufficient synchronization in MXNet iterator (#5364).
Fixed auto_reset argument handling in iterator plugins (#5340).
Fixed missing calls to nvml::Shutdown (#5317).
Limited a number of progressive scans for jpeg decoding (#5316).

Improvements

Propagate module and display name of the operator to backend (#5344)
Update dependencies (#5349)
Map backend exceptions into Python exception types (#5345)
Emphasise the optical flow is calculated at input resolution. (#5350)
Refactor custom ops classes to use python_op_factory as base (#5338)
Add origin stack trace capture for DALI operators (#5302)
Test fused decoder with two separate pipelines (#5343)
[Cutmix] Make fn.multi_paste more flexible, fix validation (#5331)
Enable checkpointing in TensorFlow plugin (CPU only) (#5334)
Copy out nvImageCodec conda package from the build (#5336)
Add error message when GPU is not available (#5329)
Enable build with statically linked nvimgcodec + hard dependency for dynamic linking (#5324)
Add tf_stack util to autograph (#5322)
Rewrite median blur to use nvcvop tools (#5327)
Add morphological operators and the nvcvop module (#5294)
Add OpSpec::ArgumentInputIdx (#5330)
Simplify workspace object. Ensure predictable argument order in OpSpec. (#5325)
Support checkpointing in MXNet iterator (#5315)
Set rpath at cmake level (do not wait for bundle-wheel) (#5323)
Interpolation modes documentation upgrade (#5314)
Update links in DALI documentation (#5321)
Integrate nvimagecodec (#5297)
Add naive_histogram custom operator to test suite (#4731)
Add RandomCropGenerator (#5304)
Use small videos in checkpointing tests (#5305)

Bug Fixes

Use synchronous copy to framework array in the absence of a stream (#5364)
Process TFRecord reader binding classes only when it is enabled (#5360)
Adjust stack formatting in backend to match Python (#5354)
Link test operators against nvml wrapper (#5355)
Fix range check in Workspace::SetInput (#5358)
Make async_pool immune to stream handle reuse. (#5348)
Coverity fixes for 1.36 (#5342)
Fix "auto_reset" argument handling (#5340)
Fix cupy tests (#5341)
Add nvimagecodec libs to DALI_EXCLUDES + test utils to dump mismatched images (#5339)
Fix warning about nvImageCodec version (#5333)
Silence warning about DOWNLOAD_EXTRACT_TIMESTAMP while fixing the cmake <3.24 builds (#5326)
Fix inconsistent calls to nvml::Init and nvml::Shutdown (#5317)
Limit the number of progressive scans for jpeg decoding (#5316)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this release.

Known issues:

The following operators: experimental.readers.fits, experimental.decoders.video, experimental.inputs.video, and experimental.decoders.image_random_crop do not currently support checkpointing.
The video loader operator requires that the key frames occur, at a minimum, every 10 to 15 frames of the video stream.
If the key frames occur at a frequency that is less than 10-15 frames, the returned frames might be out of sync.
Experimental VideoReaderDecoder does not support open GOP.
It will not report an error and might produce invalid frames. VideoReader uses a heuristic approach to detect open GOP and should work in most common cases.
The DALI TensorFlow plugin might not be compatible with TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have a prebuilt plugin binary shipped with DALI, make sure that the compiler that is used to build TensorFlow exists on the system during the plugin installation. (Depending on the particular version, you can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
In experimental debug and eager modes, the GPU external source is not properly synchronized with DALI internal streams.
As a workaround, you can manually synchronize the device before returning the data from the callback.
Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows best performance when running in Docker with escalated privileges, for example:
- privileged=yes in Extra Settings for AWS data points
- --privileged or --security-opt seccomp=unconfined for bare Docker.

Binary builds

NOTE: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

CUDA 11.0 and CUDA 12.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 11.x/12.x toolkit respectively but they can run on the latest, 
stable CUDA 11.0/CUDA 12.0 capable drivers (450.80 or later and 525.60 or later respectively).
However, using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 12.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.36.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.36.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.36.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.36.0

Or use direct download links (CUDA 12.0):

Or use direct download links (CUDA 11.0):

FFmpeg source code:

This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

https://developer.download.nvidia.com/compute/redist/nvidia-dali/libsndfile-1.2.2.tar.gz

Assets 2

27 Feb 17:33

stiepan

v1.35.0

c420f32

DALI v1.35.0

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added support for PaddlePaddle and JAX iterators (#5279, #5282).
Added support for checkpointing of iterators with different last batch policies (#5298, #5278).
Added tutorial on running DALI with T5X (#5286).
Added do_not_convert decorator to address problems with parallel fn.external_source and conditional execution (#5263).

Fixed Issues

Fixed missing nvmlShutdown calls (#5311).
Fixed fn.readers.video handling of sequences bigger than 2GB (#5307).
Fixed fn.resize handling of samples larger than 2GB (#5306).
Fixed support for multi node JAX sharding (#5242).
Fixed handling of decorated callbacks and methods in fn.external_source (#5268).
Fixed insufficient synchronization when restoring random generators from a checkpoint (#5273).

Improvements

Skip slow checkpointing tests when sanitizer is enabled (#5310)
Support checkpointing in PaddlePaddle iterator (#5279)
Bump protobuf version requirements in CMake. (#5312)
Dependency update (#5308)
Add SSL support to aarch64 deps containers (needed by CMake) (#5300)
Enable OpenSSL support (allow https downloading from cmake) (#5299)
Expose checkpointing in C API (#5287)
Enable AG-transformed code to show user code in exception (#5291)
Remove stage-related public APIs (from Pipeline and Executor) (#5244)
Add T5X tutorial (#5286)
Temporarily reduce the number of epochs in SBSA torch test (#5290)
Add a tool for correcting typos (#5193)
Support checkpointing in JAX iterator (#5282)
Expose do_not_convert decorator (#5263)
Fix Pipeline docs formatting (#5283)
Generalize iterator checkpointing tests (#5278)

Bug Fixes

Add missing calls to nvmlShutdown (#5311)
Fix bugs in iterator checkpointing, enable other last batch policies (#5298)
Fixes support for video sequences bigger than 2GB (#5307)
Fix resizing of volumes larger than 2G. (#5306)
Fix memory leak in C API test (#5303)
Fix support for multi node JAX sharding (#5242)
Fix TestPytorch (#5284)
Fix capitalized property descriptions (#5285)
Fix the lack of flavor in the conda DALI package name (#5281)
Allow source to be a decorated function or method (#5268)
Fix f-string in RN50 data pipeline test (#5280)
Remove old assumption from iterator docs (#5277)
Suppress leaks from inside dlopen (#5245)
Fix PaddlePaddle plugin docs (#5276)
Fix synchronization issues in RNG checkpointing utils (#5273)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this release.

Known issues:

The following operators: experimental.readers.fits, experimental.decoders.video, experimental.inputs.video, and experimental.decoders.image_random_crop do not currently support checkpointing.
The video loader operator requires that the key frames occur, at a minimum, every 10 to 15 frames of the video stream.
If the key frames occur at a frequency that is less than 10-15 frames, the returned frames might be out of sync.
Experimental VideoReaderDecoder does not support open GOP.
It will not report an error and might produce invalid frames. VideoReader uses a heuristic approach to detect open GOP and should work in most common cases.
The DALI TensorFlow plugin might not be compatible with TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have a prebuilt plugin binary shipped with DALI, make sure that the compiler that is used to build TensorFlow exists on the system during the plugin installation. (Depending on the particular version, you can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
In experimental debug and eager modes, the GPU external source is not properly synchronized with DALI internal streams.
As a workaround, you can manually synchronize the device before returning the data from the callback.
Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows best performance when running in Docker with escalated privileges, for example:
- privileged=yes in Extra Settings for AWS data points
- --privileged or --security-opt seccomp=unconfined for bare Docker.

Binary builds

NOTE: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

CUDA 11.0 and CUDA 12.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 11.x/12.x toolkit respectively but they can run on the latest, 
stable CUDA 11.0/CUDA 12.0 capable drivers (450.80 or later and 525.60 or later respectively).
However, using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 12.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.35.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.35.0

or just:

pip install nvidia-dali-cuda120==1.35.0
pip install nvidia-dali-tf-plugin-cuda120==1.35.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.35.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.35.0

or just:

pip install nvidia-dali-cuda110==1.35.0
pip install nvidia-dali-tf-plugin-cuda110==1.35.0

Or use direct download links (CUDA 12.0):

Or use direct download links (CUDA 11.0):

FFmpeg source code:

This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

https://developer.download.nvidia.com/compute/redist/nvidia-dali/libsndfile-1.2.2.tar.gz

Assets 2

30 Jan 17:28

stiepan

v1.34.0

a695a49

DALI v1.34.0

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added support for CUDA 12.3 U2 (#5262)
Added support for checkpointing in fn.random_resized_crop (#5246)

Fixed Issues

Fixed synchronization problem when restoring GPU random operator checkpoints (#5273).
Fixed warnings on pipeline teardown in debug mode. (#5267)
Added check for reentrant version of CFITSIO for fits reader. (#5239)
Fixed scalar inputs handling in GPU fn.lookup_table. (#5257)
Added missing validation for bboxes in fn.ssd_random_crop (#5240)
Added validation that prevents running parallel externeral source without Python workers (#5238)

Improvements

Split conda built into core and python bindings (#5259)
Dependency update - 2024/01 (#5271)
Add framework attributes to DLFW iterator tests. (#5266)
Move to CUDA 12.3 U2 (#5262)
Add links to DALI success stories in README (#5247)
Add missing imports to AA simple examples in docs (#5243)
Support checkpointing in random_resized_crop (#5246)
Add linter GitHub Action (#5236)
Adjust the error message on failed IsDenseTensor check (#5237)
Format docs directory with black (#5214)

Bug Fixes

Fix PaddlePaddle plugin docs (#5276)
Fix synchronization issues in RNG checkpointing utils (#5273)
Fix missing Shutdown method warning in debug pipeline. (#5267)
Fix issues detected by Coverity (2024.01) (#5272)
Check if reentrant version of CFITSIO is used (#5239)
Fix LookupTable GPU for scalar inputs (#5257)
Add dimension check in ssd_random_crop (#5240)
Add validation preventing running PES without Python workers (#5238)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this release.

Known issues:

The following operators: experimental.readers.fits, experimental.decoders.video, experimental.inputs.video, and experimental.decoders.image_random_crop do not currently support checkpointing.
The video loader operator requires that the key frames occur, at a minimum, every 10 to 15 frames of the video stream.
If the key frames occur at a frequency that is less than 10-15 frames, the returned frames might be out of sync.
Experimental VideoReaderDecoder does not support open GOP.
It will not report an error and might produce invalid frames. VideoReader uses a heuristic approach to detect open GOP and should work in most common cases.
The DALI TensorFlow plugin might not be compatible with TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have a prebuilt plugin binary shipped with DALI, make sure that the compiler that is used to build TensorFlow exists on the system during the plugin installation. (Depending on the particular version, you can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
In experimental debug and eager modes, the GPU external source is not properly synchronized with DALI internal streams.
As a workaround, you can manually synchronize the device before returning the data from the callback.
Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows best performance when running in Docker with escalated privileges, for example:
- privileged=yes in Extra Settings for AWS data points
- --privileged or --security-opt seccomp=unconfined for bare Docker.

Binary builds

NOTE: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

CUDA 11.0 and CUDA 12.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 11.x/12.x toolkit respectively but they can run on the latest, 
stable CUDA 11.0/CUDA 12.0 capable drivers (450.80 or later and 525.60 or later respectively).
However, using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 12.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.34.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.34.0

or just:

pip install nvidia-dali-cuda120==1.34.0
pip install nvidia-dali-tf-plugin-cuda120==1.34.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.34.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.34.0

or just:

pip install nvidia-dali-cuda110==1.34.0
pip install nvidia-dali-tf-plugin-cuda110==1.34.0

Or use direct download links (CUDA 12.0):

Or use direct download links (CUDA 11.0):

FFmpeg source code:

This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

https://developer.download.nvidia.com/compute/redist/nvidia-dali/libsndfile-1.2.2.tar.gz

Assets 2

03 Jan 11:39

stiepan

v1.33.0

c4f7645

DALI v1.33.0

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Enhanced experimental support for checkpointing (saving and resuming DALI pipelines at arbitrary iteration) (#5232, #5195, #5166):
- Support data readers checkpointing (#5198, #5213, #5184, #5182, #5165, #5181, #5180, #5183, #5162, #5139).
- Support randomized GPU operators checkpointing (#5216, #5148).
- Improved checkpointing documentation (#5230).
Improved Python annotations and signatures (#5217, #5159, #5167, #5154, #5188, #5158, #5150).
- Added annotations for JAX and Pytorch iterators (#5129, #5197).
- Improved PythonFunction annotations (#5207, #5149).
- Improved data type annotations (#5179, #5153)
Improved JAX support:
- Added pmap compatibility for JAX data_iterator (#5185).
- Improved JAX, Flax and Paxml training examples (#5176, #5205).
Moved to CUDA 12.3U1 and enbaled GDS and nvJPEG2k support for the SBSA platform (#5209, #5170).
Added Python 3.11 support and experimental support for Python 3.12 (#5174)

Fixed Issues

Fixed fn.normalize handling of batch of empty samples (#5223).
Fixed infinite video decoder seek loop (#5218).
Fixed computation of maximal threads number for kernels in GPU fn.transpose and fn.normalize. (#5208)
Fixed handling of empty slices and slicing of empty inputs. (#5204)
Fixed scalar constant dimensionality inference (#5191)
Fixed sharding in Caffe reader (#5172)

Improvements

Mark missed operator stateless, assure checkpointing tests coverage (#5232)
Add improved checkpointing docs (#5230)
Support checkpointing in Numpy reader (#5198)
Set the maximum supported Python version to Tensorflow 2.13 in tests (#5234)
Dependency update 23/12 (#5231)
Support checkpointing in External Source (#5213)
Support checkpointing in other random operators (#5216)
Remove a redundant call to Executor::GetTensorQueueSizes() (#5225)
Fix links to release notes and docs archive (#5227)
Install black with jupyter formatting capabilities (#5226)
Fix the minimal Python version for TF 2.14 and 2.15, adds 2.13 (#5221)
Unify positional input name handling in docs and signatures (#5217)
Add launch_bounds to SliceNormalizeKernel_2D kernels (#5206)
Update test_RN50_external_source_parallel_train_ddp.py to work with the latest PyTorch (#5219)
Rework Flax and Paxml training tutorials (#5205)
Mark all remaining stateless operators (#5195)
Install black formatter in the aarch64 build (#5211)
Fix typos in docs and comments (#5194)
Bump up TensorFlow version in tests (#5175)
Enbale nvJPEG2k support for the SBSA platform (#5209)
Extend Python Function and plugin type annotations (#5207)
Update readme about the Black formatting (#5212)
Add "Format DALI with black" to blame ignore revs (#5210)
Add type annotations for JAX plugin (#5197)
Fix/simplify slice usage in examples. (#5203)
Re-enable Python linter (#5189)
Format DALI with black (#5169)
Adjust configuration for autoformatting with black (#5168)
Introduce TensorLike to signatures utilizing Array Interface (#5179)
Update nvJPEG2k to 0.7.5 version (#5202)
Add doc build artifacts to gitignore (#5187)
Update FFmpeg to 6.1 (#5186)
Add pmap compatibility for JAX data_iterator (#5185)
Support checkpointing in Nemo reader (#5184)
Support checkpointing in Webdataset reader (#5182)
Support checkpointing in mxnet and tfrecord (#5165)
Support checkpointing in Caffe reader (#5181)
Support checkpointing in experimental video reader (#5180)
Support checkpointing in sequence reader (#5183)
Enable Python 3.11 test and 3.12 experimental support (#5174)
Make sure that GDS on SBSA is not tested for drivers below cuda 12.2 (#5177)
Add checkpointing benchmarks (#5166)
Improve WarpAffine input documentation (#5178)
Refactor JAX basic training example (#5176)
Support checkpointing in Coco Reader (#5162)
Move to CUDA 12.3U1 and enable GDS support for SBSA (#5170)
Expose Tensors types stubs in nvidia.dali.tensors module (#5153)
Make the video reader warn instead of failing on unreadable files (#5163)
Implement checkpointing for random GPU operators (#5148)
Generate overloads for Multiple Input Sets in interface files (#5159)
Improve API submodules discoverability in some language engines (#5167)
Expose current pipeline in python functions (#5156)
Set signature for fn API (#5154)
Add file to ignore Flake8 commits in blame (#5161)
Suppress sanitizer leaks reported from xla and prevent hang in git clone (#5152)
Add checkpointing support to Video Reader (#5139)
Add type annotations for numba and pytorch plugins (#5129)
Improve Python Function signature annotation (#5149)
Dependency update 2023.11 (#5146)
Bump up CUDA version used for tests (#5100)

Bug Fixes

Patch libtiff for CVE-2023-6277 (#5224)
Coverity fixes: fix fn.normalize handling of batch of empty samples, fix broken assertion in copy_with_stride (#5223)
Avoids infinite video decoder seek loop (#5218)
Fix and move caching from MaxThreadsPerBlock to MaxThreadsPerBlockStatic. (#5208)
Remove redundant overload signature for operators without inputs (#5188)
Fix hanlding of empty slices and slicing of empty inputs. (#5204)
Fix slice/stack/cat/scalar usage in RNNT pipeline tests. (#5201)
Fix Python3.11 tests (#5196)
Fix scalar constant dimensionality & hide constant op documentation (#5191)
Fix sharding in Caffe reader (#5172)
Fix the check for dynamically generated number of outputs (#5158)
Improve symbol visibility when importing (#5150)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this release.

Known issues:

The following operators: experimental.readers.fits, experimental.decoders.video, experimental.inputs.video, random_resized_crop, and experimental.decoders.image_random_crop do not currently support checkpointing.
The video loader operator requires that the key frames occur, at a minimum, every 10 to 15 frames of the video stream.
If the key frames occur at a frequency that is less than 10-15 frames, the returned frames might be out of sync.
Experimental VideoReaderDecoder does not support open GOP.
It will not report an error and might produce invalid frames. VideoReader uses a heuristic approach to detect open GOP and should work in most common cases.
The DALI TensorFlow plugin might not be compatible with TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have a prebuilt plugin binary shipped with DALI, make sure that the compiler that is used to build TensorFlow exists on the system during the plugin installation. (Depending on the particular version, you can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
In experimental debug and eager modes, the GPU external source is not properly synchronized with DALI internal streams.
As a workaround, you can manually synchronize the device before returning the data from the callback.
Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows best performance when running in Docker with escalated privileges, for example:
- privileged=yes in Extra Settings for AWS data points
- --privileged or --security-opt seccomp=unconfined for bare Docker.

Binary builds

NOTE: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

CUDA 11.0 and CUDA 12.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 11.x/12.x toolkit respectively but they can run on the latest, 
stable CUDA 11.0/CUDA 12.0 capable drivers (450.80 or later and 525.60 or later respectively).
However, using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 12.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.33.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.33.0

or for CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.33.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.33.0

Or use direct download links (CUDA 12.0):

Or use direct download links (CUDA 11.0):

FFmpeg source code:

This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

https://developer.d...

Assets 2

Releases: NVIDIA/DALI

DALI v1.41.0

Key Features and Enhancements

Fixed Issues

Improvements

Bug Fixes

Breaking API changes

Deprecated features

Known issues:

Binary builds

DALI v1.40.0

Key Features and Enhancements

Fixed Issues

Improvements

Bug Fixes

Breaking API changes

Deprecated features

Known issues:

Binary builds

DALI v1.39.0

Key Features and Enhancements

Fixed Issues

Improvements

Bug Fixes

Breaking API changes

Deprecated features

Known issues:

Binary builds

DALI v1.38.0

Key Features and Enhancements

Fixed Issues

Improvements

Bug Fixes

Breaking API changes

Deprecated features

Known issues:

Binary builds

DALI v1.37.1

Key Features and Enhancements

Fixed Issues

Improvements

Bug Fixes

Breaking API changes

Deprecated features

Known issues:

Binary builds

DALI v1.37.0

Key Features and Enhancements

Fixed Issues

Improvements

Bug Fixes

Breaking API changes

Deprecated features

Known issues:

Binary builds

DALI v1.36.0

Key Features and Enhancements

Fixed Issues

Improvements

Bug Fixes

Breaking API changes

Deprecated features

Known issues:

Binary builds

DALI v1.35.0

Key Features and Enhancements

Fixed Issues

Improvements

Bug Fixes

Breaking API changes

Deprecated features

Known issues:

Binary builds

DALI v1.34.0

Key Features and Enhancements

Fixed Issues

Improvements

Bug Fixes

Breaking API changes

Deprecated features